CAJ | 학술논문

针对传统 PID 控制算法在电磁导航智能车速度偏差处理中存在比例、积分、微分参数一经确定，不能在线调整、不具有自适应能力的缺点，提出了将 RBF 神经元网络控制器及其算法应用到智能车的调速系统中，对传统 PID 参数整定进行改进。RBF 神经网络能够辨识智能车电机的数学模型，可以根据控制效果在线训练和学习，调整网络连接权重值，最终自适应地整定 PID 三个参数来实现智能车的速度控制。MATLAB 仿真测试表明，与传统 PID 控制算法相比，RBF 神经网络 PID 整定算法在智能车速度控制中具有响应快，超调量小、鲁棒性和适应性强的优点，大大提高了智能车电机控制系统的性能。
침대전통 PID 공제산법재전자도항지능차속도편차처리중존재비례、적분、미분삼수일경학정，불능재선조정、불구유자괄응능력적결점，제출료장 RBF 신경원망락공제기급기산법응용도지능차적조속계통중，대전통 PID 삼수정정진행개진。RBF 신경망락능구변식지능차전궤적수학모형，가이근거공제효과재선훈련화학습，조정망락련접권중치，최종자괄응지정정 PID 삼개삼수래실현지능차적속도공제。MATLAB 방진측시표명，여전통 PID 공제산법상비，RBF 신경망락 PID 정정산법재지능차속도공제중구유향응쾌，초조량소、로봉성화괄응성강적우점，대대제고료지능차전궤공제계통적성능。
Considering that once proportional,integral and differential parameters are determined in speed deviation processing of the electromagnetically navigated intelligent vehicle according to the traditional PID control algorithm,they are not capable of online adjustment and do not have adaptive capability,this paper presents a scheme to apply the RBF neural cell network controller with its algorithm to the speed regulation system of the intelligent vehicle to improve the traditional PID parameter setting.The RBF neural network can identify the mathematical model of the intelligent car motor,conduct online training and learning according to the control effect,adjust the network connection weight and finally,adaptively adjust the three PID parameters to realize speed control over the intelligent vehicle.MATLAB simulation tests show that,compared with the traditional PID control algorithm,the PID setting algorithm of the RBF neural network has such advantages as quick response,small overshoot,robustness and strong adaptability in the speed control of the intelligent vehicle,thus greatly improving the performance of the intelligent vehicle motor control system.